Abstract
We present a system for automatic model-free fault detection based on a feature set from vibrational patterns. The complexity of the feature model is reduced by feature selection. We use a wrapper approach for the selection criteria, incorporating the training of an artificial neural network into the selection process. For fast convergence we train with the Levenberg-Marquardt algorithm. Experiments are presented for eight different fault classes.
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Rauber, T.W., Varejão, F.M. (2013). Motor Pump Fault Diagnosis with Feature Selection and Levenberg-Marquardt Trained Feedforward Neural Network. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_54
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DOI: https://doi.org/10.1007/978-3-642-40261-6_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
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